Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations10000
Missing cells780
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory264.0 B

Variable types

Text3
DateTime5
Numeric12
Categorical13

Alerts

MaturedIn 10 years is highly overall correlated with Policy Code and 2 other fieldsHigh correlation
MaturedIn 15 years is highly overall correlated with Policy Code and 1 other fieldsHigh correlation
MaturedIn 5 years is highly overall correlated with Policy Code and 2 other fieldsHigh correlation
Maturity Amount is highly overall correlated with Total Annual Premium and 3 other fieldsHigh correlation
Payment Frequency is highly overall correlated with Total Annual Premium and 2 other fieldsHigh correlation
Policy Code is highly overall correlated with MaturedIn 10 years and 4 other fieldsHigh correlation
Policy Type Code is highly overall correlated with MaturedIn 10 years and 3 other fieldsHigh correlation
Premium Amount is highly overall correlated with Total Annual PremiumHigh correlation
Premium Payment Duration is highly overall correlated with Purchase Year and 1 other fieldsHigh correlation
Purchase Month is highly overall correlated with Purchase QuarterHigh correlation
Purchase Quarter is highly overall correlated with Purchase MonthHigh correlation
Purchase Year is highly overall correlated with Premium Payment Duration and 1 other fieldsHigh correlation
RM ID is highly overall correlated with Sales Agent Code and 2 other fieldsHigh correlation
Sales Agent Code is highly overall correlated with RM ID and 2 other fieldsHigh correlation
State is highly overall correlated with RM ID and 2 other fieldsHigh correlation
Tenure (Years) is highly overall correlated with MaturedIn 10 years and 4 other fieldsHigh correlation
Total Annual Premium is highly overall correlated with Maturity Amount and 5 other fieldsHigh correlation
Total Premium Amount is highly overall correlated with Maturity Amount and 4 other fieldsHigh correlation
Total Premium Payable is highly overall correlated with Payment Frequency and 3 other fieldsHigh correlation
Total Premium paid is highly overall correlated with Maturity Amount and 6 other fieldsHigh correlation
Zonal Manager ID is highly overall correlated with RM ID and 2 other fieldsHigh correlation
profit/gain is highly overall correlated with Maturity Amount and 1 other fieldsHigh correlation
Policy Status is highly imbalanced (53.5%)Imbalance
Annualized ROI (%) has 780 (7.8%) missing valuesMissing
Sum Assured INR/Coverage Amount has unique valuesUnique
Total Premium Amount has unique valuesUnique
profit/gain has unique valuesUnique
Premium Payment Duration has 780 (7.8%) zerosZeros
Total Premium paid has 780 (7.8%) zerosZeros
Maturity Amount has 2668 (26.7%) zerosZeros

Reproduction

Analysis started2025-11-08 06:08:25.885480
Analysis finished2025-11-08 06:09:27.075493
Duration1 minute and 1.19 second
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct9938
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:27.458063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters140000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9876 ?
Unique (%)98.8%

Sample

1st rowTRS-POL-892387
2nd rowTRS-POL-603900
3rd rowTRS-POL-336174
4th rowTRS-POL-745853
5th rowTRS-POL-799138
ValueCountFrequency (%)
trs-pol-7946312
 
< 0.1%
trs-pol-3826822
 
< 0.1%
trs-pol-4626942
 
< 0.1%
trs-pol-3146572
 
< 0.1%
trs-pol-8337192
 
< 0.1%
trs-pol-3478522
 
< 0.1%
trs-pol-2992622
 
< 0.1%
trs-pol-2488742
 
< 0.1%
trs-pol-1473042
 
< 0.1%
trs-pol-7286062
 
< 0.1%
Other values (9928)9980
99.8%
2025-11-08T11:39:27.813888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
-20000
14.3%
T10000
 
7.1%
R10000
 
7.1%
S10000
 
7.1%
P10000
 
7.1%
O10000
 
7.1%
L10000
 
7.1%
66234
 
4.5%
26171
 
4.4%
76131
 
4.4%
Other values (7)41464
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)140000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-20000
14.3%
T10000
 
7.1%
R10000
 
7.1%
S10000
 
7.1%
P10000
 
7.1%
O10000
 
7.1%
L10000
 
7.1%
66234
 
4.5%
26171
 
4.4%
76131
 
4.4%
Other values (7)41464
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)140000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-20000
14.3%
T10000
 
7.1%
R10000
 
7.1%
S10000
 
7.1%
P10000
 
7.1%
O10000
 
7.1%
L10000
 
7.1%
66234
 
4.5%
26171
 
4.4%
76131
 
4.4%
Other values (7)41464
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)140000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-20000
14.3%
T10000
 
7.1%
R10000
 
7.1%
S10000
 
7.1%
P10000
 
7.1%
O10000
 
7.1%
L10000
 
7.1%
66234
 
4.5%
26171
 
4.4%
76131
 
4.4%
Other values (7)41464
29.6%
Distinct3237
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2015-07-24 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T11:39:27.964608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:28.303429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1841
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2016-03-28 00:00:00
Maximum2025-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T11:39:28.466471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:28.649683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Tenure (Years)
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.1789
Minimum10
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:28.799322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q112
median20
Q320
95-th percentile25
Maximum25
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.4460867
Coefficient of variation (CV)0.29958285
Kurtosis-1.2798103
Mean18.1789
Median Absolute Deviation (MAD)5
Skewness-0.32459948
Sum181789
Variance29.659861
MonotonicityNot monotonic
2025-11-08T11:39:28.898002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
204177
41.8%
252358
23.6%
101818
18.2%
121235
 
12.3%
15395
 
4.0%
2217
 
0.2%
ValueCountFrequency (%)
101818
18.2%
121235
 
12.3%
15395
 
4.0%
204177
41.8%
2217
 
0.2%
252358
23.6%
ValueCountFrequency (%)
252358
23.6%
2217
 
0.2%
204177
41.8%
15395
 
4.0%
121235
 
12.3%
101818
18.2%
Distinct3430
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2015-07-24 00:00:00
Maximum2025-07-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T11:39:29.039173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:29.204040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct9945
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:29.456933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters110000
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9890 ?
Unique (%)98.9%

Sample

1st rowCUST-189349
2nd rowCUST-802812
3rd rowCUST-541584
4th rowCUST-192788
5th rowCUST-221834
ValueCountFrequency (%)
cust-7881062
 
< 0.1%
cust-2932352
 
< 0.1%
cust-5275432
 
< 0.1%
cust-7166792
 
< 0.1%
cust-8695182
 
< 0.1%
cust-6709272
 
< 0.1%
cust-3847862
 
< 0.1%
cust-5844702
 
< 0.1%
cust-8672332
 
< 0.1%
cust-9063012
 
< 0.1%
Other values (9935)9980
99.8%
2025-11-08T11:39:29.866726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C10000
 
9.1%
U10000
 
9.1%
S10000
 
9.1%
T10000
 
9.1%
-10000
 
9.1%
46185
 
5.6%
26170
 
5.6%
56168
 
5.6%
96168
 
5.6%
66090
 
5.5%
Other values (5)29219
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)110000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C10000
 
9.1%
U10000
 
9.1%
S10000
 
9.1%
T10000
 
9.1%
-10000
 
9.1%
46185
 
5.6%
26170
 
5.6%
56168
 
5.6%
96168
 
5.6%
66090
 
5.5%
Other values (5)29219
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)110000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C10000
 
9.1%
U10000
 
9.1%
S10000
 
9.1%
T10000
 
9.1%
-10000
 
9.1%
46185
 
5.6%
26170
 
5.6%
56168
 
5.6%
96168
 
5.6%
66090
 
5.5%
Other values (5)29219
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)110000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C10000
 
9.1%
U10000
 
9.1%
S10000
 
9.1%
T10000
 
9.1%
-10000
 
9.1%
46185
 
5.6%
26170
 
5.6%
56168
 
5.6%
96168
 
5.6%
66090
 
5.5%
Other values (5)29219
26.6%

Sum Assured INR/Coverage Amount
Real number (ℝ)

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1054222.3
Minimum100316.82
Maximum1999546.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:30.020372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100316.82
5-th percentile196491.26
Q1577241.91
median1059397.7
Q31527198.5
95-th percentile1907294.4
Maximum1999546.8
Range1899229.9
Interquartile range (IQR)949956.59

Descriptive statistics

Standard deviation549260.89
Coefficient of variation (CV)0.52101051
Kurtosis-1.2055452
Mean1054222.3
Median Absolute Deviation (MAD)475548.59
Skewness-0.0035187733
Sum1.0542223 × 1010
Variance3.0168752 × 1011
MonotonicityNot monotonic
2025-11-08T11:39:30.268473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1296266.371
 
< 0.1%
1956818.391
 
< 0.1%
1714308.341
 
< 0.1%
1500822.071
 
< 0.1%
968973.061
 
< 0.1%
1813080.341
 
< 0.1%
1134756.631
 
< 0.1%
1139388.871
 
< 0.1%
1478517.371
 
< 0.1%
1252589.841
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
100316.821
< 0.1%
100333.641
< 0.1%
100632.551
< 0.1%
100665.031
< 0.1%
100709.941
< 0.1%
100828.111
< 0.1%
101628.271
< 0.1%
101648.771
< 0.1%
102350.251
< 0.1%
102430.361
< 0.1%
ValueCountFrequency (%)
1999546.761
< 0.1%
1999441.681
< 0.1%
1999264.171
< 0.1%
1998796.461
< 0.1%
1998525.181
< 0.1%
1998314.161
< 0.1%
1998311.261
< 0.1%
1998270.051
< 0.1%
1997605.611
< 0.1%
1997550.351
< 0.1%

Premium Amount
Real number (ℝ)

High correlation 

Distinct9995
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52261.483
Minimum5028.98
Maximum99997.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:30.513483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5028.98
5-th percentile9641.422
Q128251.645
median52362.055
Q376051.84
95-th percentile95333.279
Maximum99997.01
Range94968.03
Interquartile range (IQR)47800.195

Descriptive statistics

Standard deviation27511.888
Coefficient of variation (CV)0.52642762
Kurtosis-1.2063144
Mean52261.483
Median Absolute Deviation (MAD)23881.73
Skewness0.011607776
Sum5.2261483 × 108
Variance7.5690399 × 108
MonotonicityNot monotonic
2025-11-08T11:39:30.728046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63835.332
 
< 0.1%
58252.592
 
< 0.1%
41203.822
 
< 0.1%
52662.242
 
< 0.1%
20252.272
 
< 0.1%
90879.81
 
< 0.1%
55477.091
 
< 0.1%
41270.191
 
< 0.1%
59448.841
 
< 0.1%
56037.481
 
< 0.1%
Other values (9985)9985
99.9%
ValueCountFrequency (%)
5028.981
< 0.1%
5055.751
< 0.1%
5060.841
< 0.1%
5066.791
< 0.1%
5082.211
< 0.1%
5089.121
< 0.1%
5093.811
< 0.1%
5096.331
< 0.1%
5096.91
< 0.1%
5109.711
< 0.1%
ValueCountFrequency (%)
99997.011
< 0.1%
99976.711
< 0.1%
99974.541
< 0.1%
99965.591
< 0.1%
99958.741
< 0.1%
99944.011
< 0.1%
99938.141
< 0.1%
99932.811
< 0.1%
99925.511
< 0.1%
99920.711
< 0.1%

Payment Frequency
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Quarterly
3432 
Monthly
3324 
Annually
3244 

Length

Max length9
Median length8
Mean length8.0108
Min length7

Characters and Unicode

Total characters80108
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQuarterly
2nd rowQuarterly
3rd rowQuarterly
4th rowQuarterly
5th rowQuarterly

Common Values

ValueCountFrequency (%)
Quarterly3432
34.3%
Monthly3324
33.2%
Annually3244
32.4%

Length

2025-11-08T11:39:30.980947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:31.182707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
quarterly3432
34.3%
monthly3324
33.2%
annually3244
32.4%

Most occurring characters

ValueCountFrequency (%)
l13244
16.5%
y10000
12.5%
n9812
12.2%
r6864
8.6%
t6756
8.4%
a6676
8.3%
u6676
8.3%
Q3432
 
4.3%
e3432
 
4.3%
M3324
 
4.1%
Other values (3)9892
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)80108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l13244
16.5%
y10000
12.5%
n9812
12.2%
r6864
8.6%
t6756
8.4%
a6676
8.3%
u6676
8.3%
Q3432
 
4.3%
e3432
 
4.3%
M3324
 
4.1%
Other values (3)9892
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l13244
16.5%
y10000
12.5%
n9812
12.2%
r6864
8.6%
t6756
8.4%
a6676
8.3%
u6676
8.3%
Q3432
 
4.3%
e3432
 
4.3%
M3324
 
4.1%
Other values (3)9892
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l13244
16.5%
y10000
12.5%
n9812
12.2%
r6864
8.6%
t6756
8.4%
a6676
8.3%
u6676
8.3%
Q3432
 
4.3%
e3432
 
4.3%
M3324
 
4.1%
Other values (3)9892
12.3%

Underwriting expenses
Real number (ℝ)

Distinct9938
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5270.0628
Minimum500.6
Maximum9999.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:31.399660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500.6
5-th percentile971.745
Q12853.765
median5264.165
Q37702.4975
95-th percentile9542.81
Maximum9999.78
Range9499.18
Interquartile range (IQR)4848.7325

Descriptive statistics

Standard deviation2774.4241
Coefficient of variation (CV)0.52644991
Kurtosis-1.2283104
Mean5270.0628
Median Absolute Deviation (MAD)2422.77
Skewness-0.0086640626
Sum52700628
Variance7697429.1
MonotonicityNot monotonic
2025-11-08T11:39:31.605394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8312.832
 
< 0.1%
6058.552
 
< 0.1%
9036.622
 
< 0.1%
2861.032
 
< 0.1%
1976.242
 
< 0.1%
6276.622
 
< 0.1%
1654.92
 
< 0.1%
6541.242
 
< 0.1%
6407.72
 
< 0.1%
7090.822
 
< 0.1%
Other values (9928)9980
99.8%
ValueCountFrequency (%)
500.61
< 0.1%
500.71
< 0.1%
501.091
< 0.1%
501.271
< 0.1%
502.191
< 0.1%
502.211
< 0.1%
502.291
< 0.1%
502.371
< 0.1%
503.831
< 0.1%
504.061
< 0.1%
ValueCountFrequency (%)
9999.781
< 0.1%
9998.661
< 0.1%
9998.651
< 0.1%
9998.471
< 0.1%
9998.311
< 0.1%
9997.821
< 0.1%
9997.471
< 0.1%
9996.081
< 0.1%
9994.771
< 0.1%
9994.621
< 0.1%

Sales Agent Code
Categorical

High correlation 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
AGT-6578
1221 
AGT-3567
1120 
AGT-3488
1009 
AGT-3150
819 
AGT-2345
637 
Other values (17)
5194 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGT-6578
2nd rowAGT-6578
3rd rowAGT-6578
4th rowAGT-6578
5th rowAGT-6578

Common Values

ValueCountFrequency (%)
AGT-65781221
12.2%
AGT-35671120
11.2%
AGT-34881009
 
10.1%
AGT-3150819
 
8.2%
AGT-2345637
 
6.4%
AGT-5980612
 
6.1%
AGT-3465550
 
5.5%
AGT-4456512
 
5.1%
AGT-5467463
 
4.6%
AGT-7898458
 
4.6%
Other values (12)2599
26.0%

Length

2025-11-08T11:39:31.758810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
agt-65781221
12.2%
agt-35671120
11.2%
agt-34881009
 
10.1%
agt-3150819
 
8.2%
agt-2345637
 
6.4%
agt-5980612
 
6.1%
agt-3465550
 
5.5%
agt-4456512
 
5.1%
agt-5467463
 
4.6%
agt-7898458
 
4.6%
Other values (12)2599
26.0%

Most occurring characters

ValueCountFrequency (%)
A10000
12.5%
G10000
12.5%
T10000
12.5%
-10000
12.5%
57322
9.2%
45784
7.2%
85703
7.1%
34385
5.5%
63967
 
5.0%
93687
 
4.6%
Other values (4)9152
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A10000
12.5%
G10000
12.5%
T10000
12.5%
-10000
12.5%
57322
9.2%
45784
7.2%
85703
7.1%
34385
5.5%
63967
 
5.0%
93687
 
4.6%
Other values (4)9152
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A10000
12.5%
G10000
12.5%
T10000
12.5%
-10000
12.5%
57322
9.2%
45784
7.2%
85703
7.1%
34385
5.5%
63967
 
5.0%
93687
 
4.6%
Other values (4)9152
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A10000
12.5%
G10000
12.5%
T10000
12.5%
-10000
12.5%
57322
9.2%
45784
7.2%
85703
7.1%
34385
5.5%
63967
 
5.0%
93687
 
4.6%
Other values (4)9152
11.4%

Purchase Month
Categorical

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
December
892 
March
874 
August
866 
January
855 
July
841 
Other values (7)
5672 

Length

Max length9
Median length7
Mean length6.165
Min length3

Characters and Unicode

Total characters61650
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApril
2nd rowApril
3rd rowAugust
4th rowNovember
5th rowJune

Common Values

ValueCountFrequency (%)
December892
8.9%
March874
8.7%
August866
8.7%
January855
8.6%
July841
8.4%
November840
8.4%
October828
8.3%
May827
8.3%
April825
8.2%
September817
8.2%
Other values (2)1535
15.3%

Length

2025-11-08T11:39:31.908850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
december892
8.9%
march874
8.7%
august866
8.7%
january855
8.6%
july841
8.4%
november840
8.4%
october828
8.3%
may827
8.3%
april825
8.2%
september817
8.2%
Other values (2)1535
15.3%

Most occurring characters

ValueCountFrequency (%)
e9170
14.9%
r7423
12.0%
u4963
 
8.1%
a4157
 
6.7%
b4123
 
6.7%
y3269
 
5.3%
c2594
 
4.2%
m2549
 
4.1%
t2511
 
4.1%
J2485
 
4.0%
Other values (16)18406
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)61650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9170
14.9%
r7423
12.0%
u4963
 
8.1%
a4157
 
6.7%
b4123
 
6.7%
y3269
 
5.3%
c2594
 
4.2%
m2549
 
4.1%
t2511
 
4.1%
J2485
 
4.0%
Other values (16)18406
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)61650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9170
14.9%
r7423
12.0%
u4963
 
8.1%
a4157
 
6.7%
b4123
 
6.7%
y3269
 
5.3%
c2594
 
4.2%
m2549
 
4.1%
t2511
 
4.1%
J2485
 
4.0%
Other values (16)18406
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)61650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9170
14.9%
r7423
12.0%
u4963
 
8.1%
a4157
 
6.7%
b4123
 
6.7%
y3269
 
5.3%
c2594
 
4.2%
m2549
 
4.1%
t2511
 
4.1%
J2485
 
4.0%
Other values (16)18406
29.9%

Purchase Quarter
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Q4
2560 
Q3
2524 
Q1
2475 
Q2
2441 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ2
2nd rowQ2
3rd rowQ3
4th rowQ4
5th rowQ2

Common Values

ValueCountFrequency (%)
Q42560
25.6%
Q32524
25.2%
Q12475
24.8%
Q22441
24.4%

Length

2025-11-08T11:39:32.028085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:32.129270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
q42560
25.6%
q32524
25.2%
q12475
24.8%
q22441
24.4%

Most occurring characters

ValueCountFrequency (%)
Q10000
50.0%
42560
 
12.8%
32524
 
12.6%
12475
 
12.4%
22441
 
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q10000
50.0%
42560
 
12.8%
32524
 
12.6%
12475
 
12.4%
22441
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q10000
50.0%
42560
 
12.8%
32524
 
12.6%
12475
 
12.4%
22441
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q10000
50.0%
42560
 
12.8%
32524
 
12.6%
12475
 
12.4%
22441
 
12.2%

Purchase Year
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.0509
Minimum2015
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:32.230796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2016
Q12018
median2020
Q32023
95-th percentile2025
Maximum2025
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9134787
Coefficient of variation (CV)0.0014422798
Kurtosis-1.1553952
Mean2020.0509
Median Absolute Deviation (MAD)2
Skewness0.004066033
Sum20200509
Variance8.488358
MonotonicityNot monotonic
2025-11-08T11:39:32.387010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20181092
10.9%
20221021
10.2%
20241019
10.2%
2016994
9.9%
2019990
9.9%
2021983
9.8%
2020977
9.8%
2023974
9.7%
2017953
9.5%
2025548
5.5%
ValueCountFrequency (%)
2015449
4.5%
2016994
9.9%
2017953
9.5%
20181092
10.9%
2019990
9.9%
2020977
9.8%
2021983
9.8%
20221021
10.2%
2023974
9.7%
20241019
10.2%
ValueCountFrequency (%)
2025548
5.5%
20241019
10.2%
2023974
9.7%
20221021
10.2%
2021983
9.8%
2020977
9.8%
2019990
9.9%
20181092
10.9%
2017953
9.5%
2016994
9.9%
Distinct3428
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2016-07-24 00:00:00
Maximum2026-07-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T11:39:32.554776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:32.763685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Policy Type Code
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
TR-ENDD989
3383 
TR-WHCD968
3347 
TR-UNCD988
3270 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTR-UNCD988
2nd rowTR-UNCD988
3rd rowTR-UNCD988
4th rowTR-UNCD988
5th rowTR-UNCD988

Common Values

ValueCountFrequency (%)
TR-ENDD9893383
33.8%
TR-WHCD9683347
33.5%
TR-UNCD9883270
32.7%

Length

2025-11-08T11:39:32.968765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:33.083878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tr-endd9893383
33.8%
tr-whcd9683347
33.5%
tr-uncd9883270
32.7%

Most occurring characters

ValueCountFrequency (%)
D13383
13.4%
913383
13.4%
813270
13.3%
R10000
10.0%
T10000
10.0%
-10000
10.0%
N6653
6.7%
C6617
6.6%
E3383
 
3.4%
W3347
 
3.3%
Other values (3)9964
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D13383
13.4%
913383
13.4%
813270
13.3%
R10000
10.0%
T10000
10.0%
-10000
10.0%
N6653
6.7%
C6617
6.6%
E3383
 
3.4%
W3347
 
3.3%
Other values (3)9964
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D13383
13.4%
913383
13.4%
813270
13.3%
R10000
10.0%
T10000
10.0%
-10000
10.0%
N6653
6.7%
C6617
6.6%
E3383
 
3.4%
W3347
 
3.3%
Other values (3)9964
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D13383
13.4%
913383
13.4%
813270
13.3%
R10000
10.0%
T10000
10.0%
-10000
10.0%
N6653
6.7%
C6617
6.6%
E3383
 
3.4%
W3347
 
3.3%
Other values (3)9964
10.0%

Policy Code
Categorical

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
UGP-TRCD-9834
3205 
JSU-TRCD-9813
2358 
LGA-TRCD-9829
1818 
STP-TRCD-9812
1236 
ICP-TRCD-9832
972 
Other values (3)
411 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters130000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUGP-TRCD-9834
2nd rowUGP-TRCD-9834
3rd rowUGP-TRCD-9834
4th rowUGP-TRCD-9834
5th rowUGP-TRCD-9834

Common Values

ValueCountFrequency (%)
UGP-TRCD-98343205
32.0%
JSU-TRCD-98132358
23.6%
LGA-TRCD-98291818
18.2%
STP-TRCD-98121236
 
12.4%
ICP-TRCD-9832972
 
9.7%
WLS-TRCD-9814329
 
3.3%
RSE-TRCD-985665
 
0.7%
GWB-TRCD-983317
 
0.2%

Length

2025-11-08T11:39:33.204451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:33.371195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ugp-trcd-98343205
32.0%
jsu-trcd-98132358
23.6%
lga-trcd-98291818
18.2%
stp-trcd-98121236
 
12.4%
icp-trcd-9832972
 
9.7%
wls-trcd-9814329
 
3.3%
rse-trcd-985665
 
0.7%
gwb-trcd-983317
 
0.2%

Most occurring characters

ValueCountFrequency (%)
-20000
15.4%
911818
9.1%
T11236
 
8.6%
C10972
 
8.4%
R10065
 
7.7%
D10000
 
7.7%
810000
 
7.7%
36569
 
5.1%
U5563
 
4.3%
P5413
 
4.2%
Other values (14)28364
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)130000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-20000
15.4%
911818
9.1%
T11236
 
8.6%
C10972
 
8.4%
R10065
 
7.7%
D10000
 
7.7%
810000
 
7.7%
36569
 
5.1%
U5563
 
4.3%
P5413
 
4.2%
Other values (14)28364
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)130000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-20000
15.4%
911818
9.1%
T11236
 
8.6%
C10972
 
8.4%
R10065
 
7.7%
D10000
 
7.7%
810000
 
7.7%
36569
 
5.1%
U5563
 
4.3%
P5413
 
4.2%
Other values (14)28364
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)130000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-20000
15.4%
911818
9.1%
T11236
 
8.6%
C10972
 
8.4%
R10065
 
7.7%
D10000
 
7.7%
810000
 
7.7%
36569
 
5.1%
U5563
 
4.3%
P5413
 
4.2%
Other values (14)28364
21.8%

Policy Status
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Active
7332 
Surrendered
2524 
Lapsed
 
107
Claimed
 
37

Length

Max length11
Median length6
Mean length7.2657
Min length6

Characters and Unicode

Total characters72657
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActive
2nd rowActive
3rd rowActive
4th rowActive
5th rowActive

Common Values

ValueCountFrequency (%)
Active7332
73.3%
Surrendered2524
 
25.2%
Lapsed107
 
1.1%
Claimed37
 
0.4%

Length

2025-11-08T11:39:33.598589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:33.742700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
active7332
73.3%
surrendered2524
 
25.2%
lapsed107
 
1.1%
claimed37
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e15048
20.7%
r7572
10.4%
i7369
10.1%
A7332
10.1%
c7332
10.1%
t7332
10.1%
v7332
10.1%
d5192
 
7.1%
u2524
 
3.5%
S2524
 
3.5%
Other values (8)3100
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)72657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e15048
20.7%
r7572
10.4%
i7369
10.1%
A7332
10.1%
c7332
10.1%
t7332
10.1%
v7332
10.1%
d5192
 
7.1%
u2524
 
3.5%
S2524
 
3.5%
Other values (8)3100
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)72657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e15048
20.7%
r7572
10.4%
i7369
10.1%
A7332
10.1%
c7332
10.1%
t7332
10.1%
v7332
10.1%
d5192
 
7.1%
u2524
 
3.5%
S2524
 
3.5%
Other values (8)3100
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)72657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e15048
20.7%
r7572
10.4%
i7369
10.1%
A7332
10.1%
c7332
10.1%
t7332
10.1%
v7332
10.1%
d5192
 
7.1%
u2524
 
3.5%
S2524
 
3.5%
Other values (8)3100
 
4.3%

State
Categorical

High correlation 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Delhi
1221 
Uttar Pradesh
1120 
Sikkim
1009 
Haryana
819 
Madhya Pradesh
637 
Other values (17)
5194 

Length

Max length16
Median length13
Mean length8.8023
Min length3

Characters and Unicode

Total characters88023
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelhi
2nd rowDelhi
3rd rowDelhi
4th rowDelhi
5th rowDelhi

Common Values

ValueCountFrequency (%)
Delhi1221
12.2%
Uttar Pradesh1120
11.2%
Sikkim1009
 
10.1%
Haryana819
 
8.2%
Madhya Pradesh637
 
6.4%
Tamilnadu612
 
6.1%
Rajasthan550
 
5.5%
Bihar512
 
5.1%
Chandigarh463
 
4.6%
Uttarakhand458
 
4.6%
Other values (12)2599
26.0%

Length

2025-11-08T11:39:33.942860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh2301
18.5%
delhi1221
 
9.8%
uttar1120
 
9.0%
sikkim1009
 
8.1%
haryana819
 
6.6%
madhya637
 
5.1%
tamilnadu612
 
4.9%
rajasthan550
 
4.4%
bihar512
 
4.1%
chandigarh463
 
3.7%
Other values (14)3209
25.8%

Most occurring characters

ValueCountFrequency (%)
a17899
20.3%
h8562
 
9.7%
r7840
 
8.9%
d5240
 
6.0%
i5236
 
5.9%
e4402
 
5.0%
t4319
 
4.9%
n4169
 
4.7%
s3949
 
4.5%
k2992
 
3.4%
Other values (25)23415
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)88023
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a17899
20.3%
h8562
 
9.7%
r7840
 
8.9%
d5240
 
6.0%
i5236
 
5.9%
e4402
 
5.0%
t4319
 
4.9%
n4169
 
4.7%
s3949
 
4.5%
k2992
 
3.4%
Other values (25)23415
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)88023
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a17899
20.3%
h8562
 
9.7%
r7840
 
8.9%
d5240
 
6.0%
i5236
 
5.9%
e4402
 
5.0%
t4319
 
4.9%
n4169
 
4.7%
s3949
 
4.5%
k2992
 
3.4%
Other values (25)23415
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)88023
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a17899
20.3%
h8562
 
9.7%
r7840
 
8.9%
d5240
 
6.0%
i5236
 
5.9%
e4402
 
5.0%
t4319
 
4.9%
n4169
 
4.7%
s3949
 
4.5%
k2992
 
3.4%
Other values (25)23415
26.6%

RM ID
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
RMN7A3X1
3799 
RMC9F7L5
2018 
RMS4B9K2
1661 
RME2D5Q4
1509 
RMW8C6P3
1013 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRMN7A3X1
2nd rowRMN7A3X1
3rd rowRMN7A3X1
4th rowRMN7A3X1
5th rowRMN7A3X1

Common Values

ValueCountFrequency (%)
RMN7A3X13799
38.0%
RMC9F7L52018
20.2%
RMS4B9K21661
16.6%
RME2D5Q41509
 
15.1%
RMW8C6P31013
 
10.1%

Length

2025-11-08T11:39:34.069764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:34.179092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
rmn7a3x13799
38.0%
rmc9f7l52018
20.2%
rms4b9k21661
16.6%
rme2d5q41509
 
15.1%
rmw8c6p31013
 
10.1%

Most occurring characters

ValueCountFrequency (%)
R10000
 
12.5%
M10000
 
12.5%
75817
 
7.3%
34812
 
6.0%
N3799
 
4.7%
A3799
 
4.7%
X3799
 
4.7%
13799
 
4.7%
93679
 
4.6%
53527
 
4.4%
Other values (15)26969
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R10000
 
12.5%
M10000
 
12.5%
75817
 
7.3%
34812
 
6.0%
N3799
 
4.7%
A3799
 
4.7%
X3799
 
4.7%
13799
 
4.7%
93679
 
4.6%
53527
 
4.4%
Other values (15)26969
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R10000
 
12.5%
M10000
 
12.5%
75817
 
7.3%
34812
 
6.0%
N3799
 
4.7%
A3799
 
4.7%
X3799
 
4.7%
13799
 
4.7%
93679
 
4.6%
53527
 
4.4%
Other values (15)26969
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R10000
 
12.5%
M10000
 
12.5%
75817
 
7.3%
34812
 
6.0%
N3799
 
4.7%
A3799
 
4.7%
X3799
 
4.7%
13799
 
4.7%
93679
 
4.6%
53527
 
4.4%
Other values (15)26969
33.7%

Zonal Manager ID
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
ZMN8K2L1
5817 
ZMW3T7P8
2522 
ZMS5Q4R2
1661 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZMN8K2L1
2nd rowZMN8K2L1
3rd rowZMN8K2L1
4th rowZMN8K2L1
5th rowZMN8K2L1

Common Values

ValueCountFrequency (%)
ZMN8K2L15817
58.2%
ZMW3T7P82522
25.2%
ZMS5Q4R21661
 
16.6%

Length

2025-11-08T11:39:34.310738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:34.402413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
zmn8k2l15817
58.2%
zmw3t7p82522
25.2%
zms5q4r21661
 
16.6%

Most occurring characters

ValueCountFrequency (%)
Z10000
12.5%
M10000
12.5%
88339
10.4%
27478
9.3%
N5817
 
7.3%
K5817
 
7.3%
L5817
 
7.3%
15817
 
7.3%
W2522
 
3.2%
32522
 
3.2%
Other values (8)15871
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Z10000
12.5%
M10000
12.5%
88339
10.4%
27478
9.3%
N5817
 
7.3%
K5817
 
7.3%
L5817
 
7.3%
15817
 
7.3%
W2522
 
3.2%
32522
 
3.2%
Other values (8)15871
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Z10000
12.5%
M10000
12.5%
88339
10.4%
27478
9.3%
N5817
 
7.3%
K5817
 
7.3%
L5817
 
7.3%
15817
 
7.3%
W2522
 
3.2%
32522
 
3.2%
Other values (8)15871
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Z10000
12.5%
M10000
12.5%
88339
10.4%
27478
9.3%
N5817
 
7.3%
K5817
 
7.3%
L5817
 
7.3%
15817
 
7.3%
W2522
 
3.2%
32522
 
3.2%
Other values (8)15871
19.8%

Total Annual Premium
Real number (ℝ)

High correlation 

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296301.64
Minimum5028.98
Maximum1199964.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:34.551052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5028.98
5-th percentile20035.538
Q165387.482
median175249.96
Q3381875.01
95-th percentile1029702.6
Maximum1199964.1
Range1194935.1
Interquartile range (IQR)316487.53

Descriptive statistics

Standard deviation314418.55
Coefficient of variation (CV)1.0611435
Kurtosis0.79217685
Mean296301.64
Median Absolute Deviation (MAD)130309.79
Skewness1.3603017
Sum2.9630164 × 109
Variance9.8859025 × 1010
MonotonicityNot monotonic
2025-11-08T11:39:34.754279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255341.322
 
< 0.1%
24301.81
 
< 0.1%
237795.361
 
< 0.1%
672449.761
 
< 0.1%
80158.841
 
< 0.1%
346829.161
 
< 0.1%
168341.881
 
< 0.1%
98376.361
 
< 0.1%
306750.081
 
< 0.1%
753071
 
< 0.1%
Other values (9989)9989
99.9%
ValueCountFrequency (%)
5028.981
< 0.1%
5093.811
< 0.1%
5096.331
< 0.1%
5170.371
< 0.1%
5172.511
< 0.1%
5173.391
< 0.1%
5180.411
< 0.1%
5206.561
< 0.1%
5234.171
< 0.1%
5258.391
< 0.1%
ValueCountFrequency (%)
1199964.121
< 0.1%
1199720.521
< 0.1%
1199587.081
< 0.1%
1199257.681
< 0.1%
1199193.721
< 0.1%
1198971.241
< 0.1%
1198892.521
< 0.1%
1198877.041
< 0.1%
11986771
< 0.1%
1198274.41
< 0.1%

Total Premium Amount
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5354518.2
Minimum50289.8
Maximum29993013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:34.906605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50289.8
5-th percentile305977.84
Q11082487.5
median2817877.2
Q37257986.1
95-th percentile20050961
Maximum29993013
Range29942723
Interquartile range (IQR)6175498.6

Descriptive statistics

Standard deviation6133014.4
Coefficient of variation (CV)1.1453905
Kurtosis2.6149785
Mean5354518.2
Median Absolute Deviation (MAD)2154496.9
Skewness1.7471532
Sum5.3545182 × 1010
Variance3.7613865 × 1013
MonotonicityNot monotonic
2025-11-08T11:39:35.097082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10086746.41
 
< 0.1%
8360972.641
 
< 0.1%
570386.641
 
< 0.1%
1998433.81
 
< 0.1%
12358861
 
< 0.1%
13221302.41
 
< 0.1%
2897236.81
 
< 0.1%
1852450.81
 
< 0.1%
286908.481
 
< 0.1%
7224864.241
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
50289.81
< 0.1%
51733.91
< 0.1%
52065.61
< 0.1%
53070.31
< 0.1%
53676.51
< 0.1%
588971
< 0.1%
60457.61
< 0.1%
61013.61
< 0.1%
62810.041
< 0.1%
63273.961
< 0.1%
ValueCountFrequency (%)
299930131
< 0.1%
299896771
< 0.1%
299814421
< 0.1%
299742811
< 0.1%
299669251
< 0.1%
299568601
< 0.1%
299493511
< 0.1%
299401591
< 0.1%
299175301
< 0.1%
299172181
< 0.1%

Premium Payment Duration
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1219
Minimum0
Maximum10
Zeros780
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:35.226812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8913451
Coefficient of variation (CV)0.7014593
Kurtosis-1.0120868
Mean4.1219
Median Absolute Deviation (MAD)2
Skewness0.41546815
Sum41219
Variance8.3598764
MonotonicityNot monotonic
2025-11-08T11:39:35.329065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
31560
15.6%
21530
15.3%
11339
13.4%
7811
8.1%
0780
7.8%
5752
7.5%
6749
7.5%
4740
7.4%
8714
7.1%
9713
7.1%
ValueCountFrequency (%)
0780
7.8%
11339
13.4%
21530
15.3%
31560
15.6%
4740
7.4%
5752
7.5%
6749
7.5%
7811
8.1%
8714
7.1%
9713
7.1%
ValueCountFrequency (%)
10312
 
3.1%
9713
7.1%
8714
7.1%
7811
8.1%
6749
7.5%
5752
7.5%
4740
7.4%
31560
15.6%
21530
15.3%
11339
13.4%

Total Premium paid
Real number (ℝ)

High correlation  Zeros 

Distinct9221
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1226279.8
Minimum0
Maximum11960249
Zeros780
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:35.544590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1152425.55
median501077.43
Q31538262.9
95-th percentile5402427.5
Maximum11960249
Range11960249
Interquartile range (IQR)1385837.3

Descriptive statistics

Standard deviation1805280.1
Coefficient of variation (CV)1.4721601
Kurtosis7.1127732
Mean1226279.8
Median Absolute Deviation (MAD)430973.93
Skewness2.545756
Sum1.2262798 × 1010
Variance3.2590362 × 1012
MonotonicityNot monotonic
2025-11-08T11:39:35.722486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0780
 
7.8%
3353775.121
 
< 0.1%
303767.521
 
< 0.1%
188071.531
 
< 0.1%
505025.641
 
< 0.1%
37201.321
 
< 0.1%
45901.741
 
< 0.1%
425432.161
 
< 0.1%
287158.321
 
< 0.1%
2113335.361
 
< 0.1%
Other values (9211)9211
92.1%
ValueCountFrequency (%)
0780
7.8%
5180.411
 
< 0.1%
5206.561
 
< 0.1%
5234.171
 
< 0.1%
5272.831
 
< 0.1%
5367.651
 
< 0.1%
5560.241
 
< 0.1%
6084.131
 
< 0.1%
6444.471
 
< 0.1%
6649.681
 
< 0.1%
ValueCountFrequency (%)
11960248.81
< 0.1%
11946362.41
< 0.1%
117773041
< 0.1%
11583998.41
< 0.1%
11460394.81
< 0.1%
11294230.81
< 0.1%
11179504.81
< 0.1%
10898515.21
< 0.1%
10778457.241
< 0.1%
10770740.41
< 0.1%

Total Premium Payable
Real number (ℝ)

High correlation 

Distinct9940
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4128238.4
Minimum0
Maximum29608197
Zeros61
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:35.871381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile163144.38
Q1749094.76
median1950218.6
Q35547364.8
95-th percentile15934677
Maximum29608197
Range29608197
Interquartile range (IQR)4798270.1

Descriptive statistics

Standard deviation5086430.9
Coefficient of variation (CV)1.2321069
Kurtosis3.8062448
Mean4128238.4
Median Absolute Deviation (MAD)1555287.4
Skewness1.9681932
Sum4.1282384 × 1010
Variance2.587178 × 1013
MonotonicityNot monotonic
2025-11-08T11:39:36.062867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061
 
0.6%
1271362.561
 
< 0.1%
2853544.321
 
< 0.1%
3362248.81
 
< 0.1%
4294501.121
 
< 0.1%
6141933.61
 
< 0.1%
698564.571
 
< 0.1%
1164427.681
 
< 0.1%
9959739.841
 
< 0.1%
577809.481
 
< 0.1%
Other values (9930)9930
99.3%
ValueCountFrequency (%)
061
0.6%
7807.851
 
< 0.1%
8305.81
 
< 0.1%
10346.781
 
< 0.1%
11712.781
 
< 0.1%
16257.141
 
< 0.1%
17198.881
 
< 0.1%
17338.681
 
< 0.1%
19383.31
 
< 0.1%
19432.981
 
< 0.1%
ValueCountFrequency (%)
296081971
< 0.1%
288753211
< 0.1%
287682481
< 0.1%
286950931
< 0.1%
28538049.61
< 0.1%
28469756.161
< 0.1%
27880914.241
< 0.1%
27806673.61
< 0.1%
27785122.561
< 0.1%
276727891
< 0.1%

Maturity Amount
Real number (ℝ)

High correlation  Zeros 

Distinct7333
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4332541.2
Minimum0
Maximum34488129
Zeros2668
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-08T11:39:36.267557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1521059.6
Q35876414.8
95-th percentile19904692
Maximum34488129
Range34488129
Interquartile range (IQR)5876414.8

Descriptive statistics

Standard deviation6462753.7
Coefficient of variation (CV)1.4916774
Kurtosis4.5457543
Mean4332541.2
Median Absolute Deviation (MAD)1521059.6
Skewness2.1504481
Sum4.3325412 × 1010
Variance4.1767186 × 1013
MonotonicityNot monotonic
2025-11-08T11:39:36.441331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02668
 
26.7%
896638.581
 
< 0.1%
3686687.1721
 
< 0.1%
2154442.2841
 
< 0.1%
6717826.7521
 
< 0.1%
2441177.1361
 
< 0.1%
2467091.941
 
< 0.1%
3312469.6081
 
< 0.1%
4339682.11
 
< 0.1%
4814870.0521
 
< 0.1%
Other values (7323)7323
73.2%
ValueCountFrequency (%)
02668
26.7%
52301.3921
 
< 0.1%
53803.2561
 
< 0.1%
54148.2241
 
< 0.1%
55193.1121
 
< 0.1%
61252.881
 
< 0.1%
62875.9041
 
< 0.1%
63454.1441
 
< 0.1%
65636.49181
 
< 0.1%
66121.28821
 
< 0.1%
ValueCountFrequency (%)
34488128.551
< 0.1%
34478658.31
< 0.1%
34470423.151
< 0.1%
344503891
< 0.1%
34441753.651
< 0.1%
34431182.851
< 0.1%
34405159.51
< 0.1%
34326102.751
< 0.1%
34291892.551
< 0.1%
34186205.251
< 0.1%

Annualized ROI (%)
Text

Missing 

Distinct149
Distinct (%)1.6%
Missing780
Missing (%)7.8%
Memory size78.3 KiB
2025-11-08T11:39:36.757398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.9921909
Min length5

Characters and Unicode

Total characters55248
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row10.45%
2nd row6.69%
3rd row6.69%
4th row4.45%
5th row5.16%
ValueCountFrequency (%)
100.002307
25.0%
7.66223
 
2.4%
12.71221
 
2.4%
10.45221
 
2.4%
5.87213
 
2.3%
4.55205
 
2.2%
5.16202
 
2.2%
6.69201
 
2.2%
8.87196
 
2.1%
16.69173
 
1.9%
Other values (139)5058
54.9%
2025-11-08T11:39:37.130790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
010991
19.9%
%9220
16.7%
.9220
16.7%
16095
11.0%
63132
 
5.7%
52876
 
5.2%
72550
 
4.6%
42537
 
4.6%
-2307
 
4.2%
81973
 
3.6%
Other values (3)4347
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)55248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
010991
19.9%
%9220
16.7%
.9220
16.7%
16095
11.0%
63132
 
5.7%
52876
 
5.2%
72550
 
4.6%
42537
 
4.6%
-2307
 
4.2%
81973
 
3.6%
Other values (3)4347
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)55248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
010991
19.9%
%9220
16.7%
.9220
16.7%
16095
11.0%
63132
 
5.7%
52876
 
5.2%
72550
 
4.6%
42537
 
4.6%
-2307
 
4.2%
81973
 
3.6%
Other values (3)4347
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)55248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
010991
19.9%
%9220
16.7%
.9220
16.7%
16095
11.0%
63132
 
5.7%
52876
 
5.2%
72550
 
4.6%
42537
 
4.6%
-2307
 
4.2%
81973
 
3.6%
Other values (3)4347
 
7.9%

profit/gain
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1021976.9
Minimum-29993013
Maximum4498451.5
Zeros0
Zeros (%)0.0%
Negative2668
Negative (%)26.7%
Memory size78.3 KiB
2025-11-08T11:39:37.274575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-29993013
5-th percentile-9313602.8
Q1-409882.61
median111307.38
Q3527153.29
95-th percentile1910616.8
Maximum4498451.5
Range34491465
Interquartile range (IQR)937035.9

Descriptive statistics

Standard deviation4163831.9
Coefficient of variation (CV)-4.0742915
Kurtosis14.195781
Mean-1021976.9
Median Absolute Deviation (MAD)428687.35
Skewness-3.4807872
Sum-1.0219769 × 1010
Variance1.7337496 × 1013
MonotonicityNot monotonic
2025-11-08T11:39:37.425606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10086746.41
 
< 0.1%
-8360972.641
 
< 0.1%
-570386.641
 
< 0.1%
-1998433.81
 
< 0.1%
-12358861
 
< 0.1%
-13221302.41
 
< 0.1%
-2897236.81
 
< 0.1%
-1852450.81
 
< 0.1%
-286908.481
 
< 0.1%
-7224864.241
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
-299930131
< 0.1%
-299669251
< 0.1%
-299172181
< 0.1%
-298043611
< 0.1%
-298036951
< 0.1%
-296816731
< 0.1%
-296081971
< 0.1%
-295358251
< 0.1%
-294588721
< 0.1%
-290426191
< 0.1%
ValueCountFrequency (%)
4498451.551
< 0.1%
4497216.31
< 0.1%
4496142.151
< 0.1%
44935291
< 0.1%
4492402.651
< 0.1%
4491023.851
< 0.1%
4487629.51
< 0.1%
4477317.751
< 0.1%
4472855.551
< 0.1%
4459070.251
< 0.1%
Distinct5538
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2025-01-08 00:00:00
Maximum2049-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T11:39:37.574206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:37.729382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MaturedIn 5 years
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
8554 
1
1446 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08554
85.5%
11446
 
14.5%

Length

2025-11-08T11:39:37.864905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:37.945239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
08554
85.5%
11446
 
14.5%

Most occurring characters

ValueCountFrequency (%)
08554
85.5%
11446
 
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08554
85.5%
11446
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08554
85.5%
11446
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08554
85.5%
11446
 
14.5%

MaturedIn 10 years
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
7871 
1
2129 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07871
78.7%
12129
 
21.3%

Length

2025-11-08T11:39:38.053827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:38.116157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
07871
78.7%
12129
 
21.3%

Most occurring characters

ValueCountFrequency (%)
07871
78.7%
12129
 
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07871
78.7%
12129
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07871
78.7%
12129
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07871
78.7%
12129
 
21.3%

MaturedIn 15 years
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
7068 
1
2932 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
07068
70.7%
12932
29.3%

Length

2025-11-08T11:39:38.197179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T11:39:38.260951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
07068
70.7%
12932
29.3%

Most occurring characters

ValueCountFrequency (%)
07068
70.7%
12932
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07068
70.7%
12932
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07068
70.7%
12932
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07068
70.7%
12932
29.3%

Interactions

2025-11-08T11:39:24.023433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:53.893294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:57.623770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:00.456610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:04.828554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:08.854623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:11.957483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:14.374234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:16.228101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:17.801336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:19.962703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:21.976238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:24.139972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:54.219090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:57.804450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:00.836112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:05.022026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:09.218652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:12.188280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:14.504696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:16.358256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:18.003607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:20.148154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:22.102889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:24.272482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:54.394303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:57.962379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:01.330383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:05.384620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:09.492532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:12.388681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:14.696491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:16.497500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:18.141430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:20.349890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:22.290227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:24.388431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:54.569943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:58.220436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:01.492646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:05.755156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:09.755465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:12.740132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:14.836948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:16.615484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:18.276924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:20.549211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:22.481142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:24.559566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:55.334524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:58.423128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:01.757588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:06.058069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:10.092358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:12.987345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:14.995767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:16.767861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:18.434026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:20.793812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:22.641608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:24.750228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:55.768575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:58.598587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:02.035811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:06.363613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:10.395785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:13.161341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:15.116058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:16.894852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:18.575717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:20.959050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:22.801577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:24.940271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:56.110477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:58.766120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:02.308562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:06.530714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:10.593446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:13.437691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:15.272936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:17.020896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:18.703257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:21.101522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:23.118973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:25.131835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:56.450026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:58.958749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:02.599468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:06.769151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:10.942110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:13.625320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:15.418359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:17.144099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:18.907885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:21.266296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:23.279974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:25.291901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:56.838138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:59.115760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:03.347378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:07.187858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:11.162595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:13.756141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:15.538725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:17.254830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:19.082907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:21.394168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:23.418111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:25.447149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:57.048709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:59.340739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:03.972257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:07.620554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:11.308251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:13.920480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:15.720676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:17.381269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:19.320206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:21.525976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:23.565762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:25.642763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:57.282104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:59.585084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:04.391314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:08.097894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:11.482033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:14.067111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:15.873912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:17.538152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:19.517052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:21.680494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:23.731418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:25.818280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:57.476479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:38:59.885231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:04.644203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:08.532151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:11.800751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:14.233385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:16.033980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:17.679311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:19.756809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:21.839118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T11:39:23.873908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-08T11:39:38.369849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
MaturedIn 10 yearsMaturedIn 15 yearsMaturedIn 5 yearsMaturity AmountPayment FrequencyPolicy CodePolicy StatusPolicy Type CodePremium AmountPremium Payment DurationPurchase MonthPurchase QuarterPurchase YearRM IDSales Agent CodeStateSum Assured INR/Coverage AmountTenure (Years)Total Annual PremiumTotal Premium AmountTotal Premium PayableTotal Premium paidUnderwriting expensesZonal Manager IDprofit/gain
MaturedIn 10 years1.0000.1510.0210.1310.0110.6240.0360.6130.0190.2330.0180.0230.2020.0280.0510.0510.0000.6240.0030.1580.1630.0470.0240.0160.172
MaturedIn 15 years0.1511.0000.2640.0850.0000.5270.0350.3740.0000.3080.0240.0290.3780.0870.1740.1740.0000.5270.0000.1090.1340.0980.0100.0880.038
MaturedIn 5 years0.0210.2641.0000.1290.0130.6280.0000.5720.0000.3280.0610.0560.3940.0160.0000.0000.0000.6280.0000.1610.2340.1510.0000.0000.164
Maturity Amount0.1310.0850.1291.0000.4830.1130.2520.1780.2780.3640.0000.012-0.0070.0100.0320.032-0.0120.1350.5100.5320.4370.5860.0040.0190.988
Payment Frequency0.0110.0000.0130.4831.0000.0130.0070.0100.0000.0000.0220.0200.0000.0090.0260.0260.0190.0000.7210.6220.5440.4260.0000.0040.438
Policy Code0.6240.5270.6280.1130.0131.0000.1231.0000.0100.0120.0000.0050.0000.1630.2320.2320.0001.0000.0130.1350.1240.0010.0240.2170.125
Policy Status0.0360.0350.0000.2520.0070.1231.0000.0000.0050.3190.0000.0000.0000.1380.2980.2980.0100.0860.0030.0000.0470.1250.0070.0720.404
Policy Type Code0.6130.3740.5720.1780.0101.0000.0001.0000.0070.0000.0000.0110.0050.0040.0360.0360.0000.8710.0110.2110.2130.0000.0000.0040.205
Premium Amount0.0190.0000.0000.2780.0000.0100.0050.0071.0000.0100.0090.003-0.0150.0150.0060.006-0.007-0.0090.5260.4990.4630.3870.0080.0230.236
Premium Payment Duration0.2330.3080.3280.3640.0000.0120.3190.0000.0101.0000.0730.120-0.8270.0230.0330.033-0.0090.0180.0040.008-0.1900.5960.0150.0160.359
Purchase Month0.0180.0240.0610.0000.0220.0000.0000.0000.0090.0731.0001.0000.0820.0090.0000.0000.0170.0060.0130.0090.0120.0190.0150.0130.003
Purchase Quarter0.0230.0290.0560.0120.0200.0050.0000.0110.0030.1201.0001.0000.1330.0120.0000.0000.0220.0070.0050.0170.0050.0280.0080.0060.000
Purchase Year0.2020.3780.394-0.0070.0000.0000.0000.005-0.015-0.8270.0820.1331.0000.0090.0050.0050.010-0.009-0.015-0.0160.150-0.507-0.0160.000-0.006
RM ID0.0280.0870.0160.0100.0090.1630.1380.0040.0150.0230.0090.0120.0091.0000.9990.9990.0000.1080.0090.0170.0000.0000.0001.0000.021
Sales Agent Code0.0510.1740.0000.0320.0260.2320.2980.0360.0060.0330.0000.0000.0050.9991.0001.0000.0000.1830.0130.0290.0230.0000.0050.9990.041
State0.0510.1740.0000.0320.0260.2320.2980.0360.0060.0330.0000.0000.0050.9991.0001.0000.0000.1830.0130.0290.0230.0000.0050.9990.041
Sum Assured INR/Coverage Amount0.0000.0000.000-0.0120.0190.0000.0100.000-0.007-0.0090.0170.0220.0100.0000.0000.0001.000-0.007-0.014-0.015-0.012-0.019-0.0090.000-0.012
Tenure (Years)0.6240.5270.6280.1350.0001.0000.0860.871-0.0090.0180.0060.007-0.0090.1080.1830.183-0.0071.000-0.0230.2250.3270.0010.0090.1450.159
Total Annual Premium0.0030.0000.0000.5100.7210.0130.0030.0110.5260.0040.0130.005-0.0150.0090.0130.013-0.014-0.0231.0000.9630.8880.7360.0020.0100.423
Total Premium Amount0.1580.1090.1610.5320.6220.1350.0000.2110.4990.0080.0090.017-0.0160.0170.0290.029-0.0150.2250.9631.0000.9550.7150.0040.0200.452
Total Premium Payable0.1630.1340.2340.4370.5440.1240.0470.2130.463-0.1900.0120.0050.1500.0000.0230.023-0.0120.3270.8880.9551.0000.544-0.0000.0000.367
Total Premium paid0.0470.0980.1510.5860.4260.0010.1250.0000.3870.5960.0190.028-0.5070.0000.0000.000-0.0190.0010.7360.7150.5441.0000.0060.0000.523
Underwriting expenses0.0240.0100.0000.0040.0000.0240.0070.0000.0080.0150.0150.008-0.0160.0000.0050.005-0.0090.0090.0020.004-0.0000.0061.0000.0050.004
Zonal Manager ID0.0160.0880.0000.0190.0040.2170.0720.0040.0230.0160.0130.0060.0001.0000.9990.9990.0000.1450.0100.0200.0000.0000.0051.0000.033
profit/gain0.1720.0380.1640.9880.4380.1250.4040.2050.2360.3590.0030.000-0.0060.0210.0410.041-0.0120.1590.4230.4520.3670.5230.0040.0331.000

Missing values

2025-11-08T11:39:26.177541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-08T11:39:26.640984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Policy NumberStart DateLast Paid DateTenure (Years)Date of PurchaseCustomer IDSum Assured INR/Coverage AmountPremium AmountPayment FrequencyUnderwriting expensesSales Agent CodePurchase MonthPurchase QuarterPurchase YearPolicy Anniversary DatePolicy Type CodePolicy CodePolicy StatusStateRM IDZonal Manager IDTotal Annual PremiumTotal Premium AmountPremium Payment DurationTotal Premium paidTotal Premium PayableMaturity AmountAnnualized ROI (%)profit/gainTenure DateMaturedIn 5 yearsMaturedIn 10 yearsMaturedIn 15 years
0TRS-POL-89238718 April 202218 April 20252018 April 2022CUST-189349619301.1742085.47Quarterly3335.88AGT-6578AprilQ2202218 April 2023TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1168341.883366837.63505025.642861811.963686687.17210.45%319849.57218-04-2042000
1TRS-POL-60390019 April 201919 April 20252019 April 2019CUST-8028121330929.4424594.09Quarterly7587.61AGT-6578AprilQ2201919 April 2020TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L198376.361967527.26590258.161377269.042154442.2846.69%186915.08419-04-2039001
2TRS-POL-33617412 August 201912 August 20252012 August 2019CUST-541584714993.6276687.52Quarterly4288.23AGT-6578AugustQ3201912 August 2020TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1306750.086135001.661840500.484294501.126717826.7526.69%582825.15212-08-2039001
3TRS-POL-74585314 November 201614 November 20252014 November 2016CUST-192788270848.2618826.75Quarterly6725.00AGT-6578NovemberQ4201614 November 2017TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L175307.001506140.09677763.00828377.001619100.5004.45%112960.50014-11-2036001
4TRS-POL-79913809 June 201709 June 20252009 June 2017CUST-2218341885143.7493979.29Quarterly5369.76AGT-6578JuneQ2201709 June 2018TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1375917.167518343.283007337.284511005.928232585.8045.16%714242.60409-06-2037001
5TRS-POL-16114708 March 202408 March 20242020 April 2025CUST-114960954941.3410426.03Quarterly5087.14AGT-6578AprilQ2202520 April 2026TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L141704.12834082.400.00834082.40896638.580NaN62556.18008-03-2044000
6TRS-POL-76587112 May 202112 May 20252012 May 2021CUST-3606961765342.5028163.15Quarterly825.98AGT-6578MayQ2202112 May 2022TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1112652.602253052.04450610.401802441.602467091.9408.87%214039.94012-05-2041000
7TRS-POL-74655515 February 202115 February 20252015 February 2021CUST-876796503342.8037813.58Quarterly6045.14AGT-6578FebruaryQ1202115 February 2022TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1151254.323025086.44605017.282420069.123312469.6088.87%287383.20815-02-2041000
8TRS-POL-11014331 July 202031 July 20252031 July 2020CUST-4167151994578.6649539.75Quarterly3456.37AGT-6578JulyQ3202031 July 2021TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1198159.003963180.05990795.002972385.004339682.1007.66%376502.10031-07-2040001
9TRS-POL-74223625 May 201725 May 20252025 May 2017CUST-9645991170360.8754964.27Quarterly2759.54AGT-6578MayQ2201725 May 2018TR-UNCD988UGP-TRCD-9834ActiveDelhiRMN7A3X1ZMN8K2L1219857.084397141.681758856.642638284.964814870.0525.16%417728.45225-05-2037001
Policy NumberStart DateLast Paid DateTenure (Years)Date of PurchaseCustomer IDSum Assured INR/Coverage AmountPremium AmountPayment FrequencyUnderwriting expensesSales Agent CodePurchase MonthPurchase QuarterPurchase YearPolicy Anniversary DatePolicy Type CodePolicy CodePolicy StatusStateRM IDZonal Manager IDTotal Annual PremiumTotal Premium AmountPremium Payment DurationTotal Premium paidTotal Premium PayableMaturity AmountAnnualized ROI (%)profit/gainTenure DateMaturedIn 5 yearsMaturedIn 10 yearsMaturedIn 15 years
9990TRS-POL-91383628 August 202128 August 20211228 August 2021CUST-399205606829.6511862.97Quarterly5943.52AGT-4456AugustQ3202128 August 2022TR-ENDD989STP-TRCD-9812LapsedBiharRMC9F7L5ZMN8K2L147451.88569422.5600.00569422.560.0NaN-569422.5628-08-2033010
9991TRS-POL-75245528 March 201628 March 20161228 March 2016CUST-639678604946.7173364.96Annually6338.24AGT-4456MarchQ1201628 March 2017TR-ENDD989STP-TRCD-9812ClaimedBiharRMC9F7L5ZMN8K2L173364.96880379.5200.00880379.520.0NaN-880379.5228-03-2028100
9992TRS-POL-98163810 June 201610 June 20161210 June 2016CUST-2484771543871.1839037.94Monthly4564.53AGT-4456JuneQ2201610 June 2017TR-ENDD989STP-TRCD-9812ClaimedBiharRMC9F7L5ZMN8K2L1468455.285621463.3600.005621463.360.0NaN-5621463.3610-06-2028100
9993TRS-POL-65599521 July 201721 July 20201521 July 2017CUST-3483361198318.3456748.62Annually3099.95AGT-4456JulyQ3201721 July 2018TR-UNCD988RSE-TRCD-9856LapsedBiharRMC9F7L5ZMN8K2L156748.62851229.303170245.86680983.440.0-100.00%-851229.3021-07-2032010
9994TRS-POL-27498921 March 202221 March 20222221 March 2022CUST-516693608369.7919599.33Quarterly2347.60AGT-4456MarchQ1202221 March 2023TR-WHCD968GWB-TRCD-9833LapsedBiharRMC9F7L5ZMN8K2L178397.321724741.0400.001724741.040.0NaN-1724741.0421-03-2044000
9995TRS-POL-55958419 July 202419 July 20241219 July 2024CUST-923180349590.3937745.08Monthly4595.54AGT-4456JulyQ3202419 July 2025TR-ENDD989STP-TRCD-9812ClaimedBiharRMC9F7L5ZMN8K2L1452940.965435291.5200.005435291.520.0NaN-5435291.5219-07-2036001
9996TRS-POL-84164212 November 202012 November 20201212 November 2020CUST-741949296895.3915641.59Annually1729.38AGT-4456NovemberQ4202012 November 2021TR-ENDD989STP-TRCD-9812ClaimedBiharRMC9F7L5ZMN8K2L115641.59187699.0800.00187699.080.0NaN-187699.0812-11-2032010
9997TRS-POL-95784308 August 201608 August 20192208 August 2016CUST-9391991714215.9662690.51Annually2547.18AGT-4456AugustQ3201608 August 2017TR-WHCD968GWB-TRCD-9833LapsedBiharRMC9F7L5ZMN8K2L162690.511379191.223188071.531191119.690.0-100.00%-1379191.2208-08-2038001
9998TRS-POL-91944601 March 202201 March 20221501 March 2022CUST-451502626672.0922644.85Quarterly2717.31AGT-4456MarchQ1202201 March 2023TR-UNCD988RSE-TRCD-9856ClaimedBiharRMC9F7L5ZMN8K2L190579.401358691.0000.001358691.000.0NaN-1358691.0001-03-2037001
9999TRS-POL-18040126 December 202126 December 20211226 December 2021CUST-7031271866944.9628902.43Monthly6965.41AGT-4456DecemberQ4202126 December 2022TR-ENDD989STP-TRCD-9812LapsedBiharRMC9F7L5ZMN8K2L1346829.164161949.9200.004161949.920.0NaN-4161949.9226-12-2033010